The Cultural Brain Hypothesis is a more general theory for brain evolution across species that unifies more specific explanations around environmental hypotheses and social brain hypotheses. The theory is formalized using an analytical and a computational model.

Figure 1 from paper

The CBH shows how the environment constrains evolution and how social factors are necessary infrastructure for more social learning species. It predicts different relationships between brain size, sociality, mating structure, the length of the juvenile period, innovation and knowledge, and social learning strategies.

Table 1 from paper.

According to the CBH, the environment constrains brain evolution rather than driving it – brain size is affected by the environment, because you need to have enough calories to feed your brain. But your ability to derive calories from what’s available (or potentially available) is driven by how smart you are – how much information you have. All else being equal, a lush rainforest will have larger brains than an arid desert.

The model specifies two pathways for acquiring this information, both of which can lead to bigger brains – asocial learning and social learning (or some combination of these). If you take the asocial path, you’re reliant on your own intelligence and you don’t have to worry about the social infrastructure. Asocial brains can be larger depending on how easy it is to learn things asocially, but they’ll tend to be smaller than social brains on average.

If you take the social path, it requires all kinds of social infrastructure – more tight-knit and perhaps larger group to learn from, a longer juvenile period, more care during that longer juvenile period, tolerance for other members of the group, an ability and proclivity to learn from other members of the group, and so on. Culture is socially transmitted information, which is a cheaper and more efficient way to get information than asocial learning, but does require all these social factors.

The theory links together ecology and social factors and shows how constraints for learning culture and information in general are what drive the expansion in brain evolution (rather than adaptations to the environment or social factors directly). The model allows us to make sense of a lot of puzzling relationships between brain size, sociality, mating structures, juvenile period, innovation, knowledge, and social learning strategies, and other social and environmental features. We’ve tested some of these relationships among cetaceans and in this paper, we compare it to tests in primates. Unfortunately, most of the focus has been on the more interesting more social learning species (you publish papers by showing how animals and babies are smart and human adults are dumb, not vice versa). The next step is to try to test the predictions for more asocial taxa.

The Cumulative Cultural Brain Hypothesis (CCBH)

The CCBH is a narrow set of parameters that can lead to a take off where information and technology start accumulating faster and faster forcing brains and social factors to evolve to keep up. In our species, our brains continue to grow to the point where we end having trouble giving birth to our babies (larger heads are more difficult to birth), we give birth to our babies prematurely relative to other animals (compare a human infant to a gazelle ready to run). This leads to strategies to take care of our now helpless infants, like forcing fathers to pay for childcare or stick around, and normatively controlling female sexuality so dad knows it’s his. We do other things to keep up. We divide up the information, leading to a division of information and a division of labor (specialization), which can lead to a collective brain. We expand our juvenile period, so we spend longer in childhood, and have an extraordinarily long period of adolescence (the time between when you can reproduce and when you actually do), just to keep learning the ever growing body of information needed to outcompete other members f our group. This last strategy is now at the point we’re hitting a new biological limit – not in the size of the brains we can birth, but in our ability to reproduce at a later age. (I wrote a bit about this for MoneySupermarket in reference to why it takes longer to buy a house).

According to the CCBH, this take off requires:

High transmission fidelity. This could include more cognitive abilities like gaze tracking, shared intentionality, theory of mind, the ability to recognize, distinguish, and imitate potential models, but also more social factors like social tolerance, and ever more sophisticated methods of teaching (consider how long you’ve probably spent in formal education plus internships or low paid entry-level jobs).

Low reproductive skew. Consistent with a “monogamish” or cooperative breeding structure that suppresses reproductive skew. A cooperative breeding environment would have also been ideal to allow for an easy transition to oblique learning. Chimps learn from their mom, but having multiple moms and dads means you can focus on who’s better rather than who you have access to.

Smart ancestors. There is an interaction between transmission fidelity and efficient individual learning. Social learners benefit from smart asocial learners who’s knowledge they can exploit.

Rich ecology. There have to be potential returns in the environment. That is, there are large game or good sources of calories, only requiring the knowledge to acquire them.

Last week, my paper with Kieran Fox and Susanne Shultz was published in Nature Ecology and Evolution. The paper was a multiyear project, which consisted of countless hours spent poring through marine mammal literature to create the most comprehensive database of cetacean physiology, social structure, life history, and behavior to date. We then used this database to test some of the predictions of the Social Brain and Cultural Brain Hypotheses. Some of the confirmations of these predictions are shown in Figure 3 of the paper below.

Cetaceans represent a great test for the Social Brain and Cultural Brain Hypotheses (CBH), because of how evolutionarily alien these species are, and how strange their underwater world is compared to the world we inhabit. We have previously tested the CBH predictions with primates, but their evolutionary closeness to humans means that the relationships we find may be due to our evolutionary logic or due to these features (such as large brains and high sociality) being present in a common ancestor. Thus finding these relationships in cetaceans is strong evidence for the evolutionary logic. It also sets up cetaceans as an interesting control group for understanding human evolution.

Introducing the possibility of bribes into an institutional punishment public goods game results in reduced contributions.

In an institutional punishment public goods game, stronger leaders result in more cooperation. In our modified “bribery game”, stronger leaders result in less cooperation.

Anti-corruption measures including transparency and tying leaders payoffs to the success of the public good result improve contributions, except if economic potential is low and leaders are weak. Here, they can actually further reduce contributions.

Culture matters. Exposure to corrupt norms via living in corrupt places increases bribes, but having an ethnic heritage that includes corrupt countries, but not having actually lived there yourself results in less bribery.

Figures 1, 2 and 3, reproduced below illustrate these results.

Raw contributions (of the ten endowed points) and 95% confidence intervals for each within-subject treatment (control, BG, BG with partial transparency or BG with full transparency) in each between-subjects structural context (strong versus weak leader and poor versus rich economic potential). These data are consistent with our theory that predicts that more powerful leaders increase contributions in the IPGG but decrease contributions in the BG.

Darker blue indicates greater public goods provisioning and darker red indicates reduced public goods provisioning. All coefficients were extracted from a single model by changing reference groups. The columns represent the reference group treatment (control versus BG), while each row shows the coefficient of each treatment compared with this reference group. The contributions were z scores, so the coefficients represent s.d. The full model is reported in the Supplementary Information. In all models, we accounted for the clustering inherent in the experimental design by including a fixed effect for the number of subjects and random effects for participants within groups. Note that in all treatments and structural contexts, the BG has lower contributions than the structurally equivalent IPGG (control). Corruption mitigation effectively increases contributions (although not to control levels) when leaders are strong or the economic potential is rich. When leaders are weak and the economic potential is poor, the apparent corruption mitigation strategy, full transparency has no effect and partial transparency further decreases contributions. *P < 0.10; **P < 0.05; ***P < 0.01; ****P < 0.001.

Odds ratios and 95% confidence intervals are shown for each behaviour (accept bribe, punish or do nothing).

Corruption is Rooted in Our Relationships

There is nothing natural1 about democracy. There is nothing natural about living in communities with complete strangers. There is nothing natural about large-scale anonymous cooperation. Yet, this morning, I bought a coffee from Starbucks with no fear of being poisoned or cheated. I caught a train on London’s underground packed with people I’ve never met before and will probably never meet again. If we were commuting chimps in a space that small, it would have been a scene out of the latest Planet of the Apes by the time we reached Holborn station. We’ll return to this mystery in a moment.

There is something very natural about prioritizing your family over other people. There is something very natural about helping your friends and others in your social circle. And there is something very natural about returning favors given to you. These are all smaller scales of cooperation that we share with other animals and that are well described by the math of evolutionary biology. The trouble is that these smaller scales of cooperation can undermine the larger-scale cooperation of modern states. Although corruption is often thought of as a falling from grace, a challenge to the normal functioning state—it’s in the etymology of the word—it’s perhaps better understood as the flip side of cooperation. One scale of cooperation, typically the one that’s smaller and easier to sustain, undermines another.

When a leader gives his daughter a government contract, it’s nepotism. But it’s also cooperation at the level of the family, well explained by inclusive fitness2, undermining cooperation at the level of the state. When a manager gives her friend a job, it’s cronyism. But it’s also cooperation at the level of friends, well explained by reciprocal altruism3, undermining the meritocracy. Bribery is a cooperative act between two people, and so on. It’s no surprise that family-oriented cultures like India and China are also high on corruption, particularly nepotism. Even in the Western world, it’s no surprise that Australia, a country of mates, might be susceptible to cronyism. Or that breaking down kin networks predicts lower corruption and more successful democracies (Akbari, Bahrami-Rad & Kimbrough, 2017; Schulz, 2017). Part of the problem is that these smaller scales of cooperation are easier to sustain and explain than the kind of large-scale anonymous cooperation that we in the Western world have grown accustomed to.

So how is it that some states prevent these smaller scales of cooperation from undermining large-scale anonymous cooperation? The typical answer is that more successful nations have better institutions. All that’s required is the right set of rules to make society function. But even on the face of it, this answer seems incomplete. If it were true, Liberia, who borrowed more than its flag from the United States, ought to be much more successful than it is4. Instead, these institutions are supported by invisible cultural pillars without which the institutions would fail. For example, without a belief in rule of law—that the law applies to all and cannot be changed on the whim of the leader—it doesn’t matter what the constitution or legal code says, no one is listening. Without a long time horizon, decisions are judged on how well they serve our immediate needs making larger-scale projects, like reducing the effects of Climate Change, harder to justify5. Similarly, institutions often lack the punitive power to actually punish perpetrators. For example, most people in the US and UK pay their taxes, even though in reality the IRS and Her Majesty’s Revenue and Customs lack the power to prosecute widespread non-compliance; your probability of getting caught is low. The tax compliant majority may never discover that they can cheat or how to get away with it (Chetty, et al. 2013) and they may not actively seek this information as long as the probability of getting caught is non-zero, the system seems fair, and it seems like everyone else is complying. Or in other words, it’s a combination of norms and institutions. But, it gets tricky—institutions are themselves hardened or codified norms6 and the norms themselves evolve in response to the present environment and due to path-dependence of previous environments, past decisions, and the places migrants come from. Modern groups vary on individualism (Talhelm, et al., 2014) and even sexist attitudes (Alesina, et al., 2013) based on their ancestors’ farming practices7. The science of cultural evolution describes the evolution of these norms and introduces the possibility of out-of-equilibria behavior (people behaving in ways that do not benefit them individually) for long enough for institutions to try to stabilize the new equilibria. For a summary of cultural evolution, see Joseph Henrich’s excellent book and for an even shorter summary see this chapter). How do we begin to understand these processes?

The real world is messy and before we start running randomized control trials or preparing case studies, it’s useful to model the basic dynamics of cooperation using a simpler form that gets at the core elements of the challenge. One commonly used model is called the “Public Goods Game”. The gist of the game is that I give you, and say 9 others, $10. Whatever you put into a pool (the public good), I’ll multiply by say 3, but then I’ll divide the money equally regardless of contribution. This is similar to paying your taxes for public goods that we all benefit from, like roads, clean water, or environmental protections. The dilemma is this: the best move is for everyone to put all their money in the pool. Then they’ll all go home with $30. But it’s in my best interests to put nothing in the pool and let everyone else put their money in. If I put in nothing and they put in $10 each, I’ll go home with almost $40 ($10*9*3people / 10 = $37). What happens when we play this game?

Well, if we play it in a WEIRD8 nation, where prosocial norms tend to be higher, people put about half their money in, but as they gradually realize they can make more by putting in less, contributions dwindle to zero. One way to sustain contributions is to introduce peer punishment—allow people to spend some portion of their money to punish other people. This is similar to the kind of punishment we might see in a small village. I know who you are or at least I know your parents or people you know. If you steal my crops, I’ll punish you myself or ruin your reputation. In the game, if we introduce the possibility of peer punishment, contributions rise again. The problem is that this doesn’t scale well. As the number of people grows, we get second-order free-riding—people prefer to let someone else pay the cost of punishment. When someone cuts a queue, you grumble—someone ought to tell that person off! Someone other than me… And you can also get counter-punishment—revenge for being punished. The best solution seems to be to create a punishment institution. Pick one person as a “Leader” and allow them to extract taxes that can be used to punish free-riders. This works really well and scales up nicely. It’s similar to a functioning police force and judiciary in WEIRD nations. In fact, the models suggest that the more power you give to the leader, the more cooperation they can sustain. Aha! Problem solved. Not quite. Models like these are very useful for distilling the core of a phenomenon, they can miss things. Recall where we started—smaller-scales of cooperation can undermine the larger-scale.

In our recently published paper, we wanted to show just how easy it was to break that well-functioning institution. We did it by introducing the possibility of another very simple form of cooperation—you scratch my back, I’ll scratch yours—bribery. And then we wanted to show the power of invisible cultural pillars by measuring people’s cultural background and by trying to fix corruption using common anti-corruption strategies. We wanted to show that these strategies, including transparency, don’t work in all contexts and can even backfire.

Our “Bribery Game” was the usual institutional punishment public goods game with the punishing leader, but with one additional choice—players could not only keep money for themselves or contribute to the public pool, they could also contribute to the leader. And the leader could not only punish or not punish, they could instead accept that contribution. What happened? On average, we saw contributions fall by 25% compared to the game without bribery as an option. More than double what the pound has fallen against the USD since Brexit (~12%9). Fine, bribery is costly. The World Bank estimates $1 trillion is paid in bribes alone; in Kenya, 8 out of 10 interactions with public officials involves a bribe, and as Manfred Milinski points out in his summary of our paper, most of humanity—6 billion people—live in nations with high levels of corruption. Our model also reveals that unlike the typical institutional punishment public goods game, where stronger institutions mean that more cooperation can be sustained, when bribery is an option, stronger institutions mean more bribery. A small bribe multiplied by the number of players will make you a lot richer than your share of the public good! So can we fix it?

The usual answer is transparency. There are also some interesting approaches, like tying a leader’s salary to the country’s GDP—the Singaporean model10. So what happened when we introduced these strategies? Well, when the public goods multiplier was high (economic potential—potential to make money using legitimate means—was high) or the institution had power to punish, then contributions went up. Not to levels without bribery as an option, but higher. But in poor contexts with weak punishing institutions, transparency had no effect or backfired. As did the Singaporean model11. Why? Consider what transparency does. It tells us what people are doing. But as psychological and cultural evolutionary research reveals, this solves a common knowledge problem and reveals the descriptive norm—what people are doing. For it to have any hope of changing behavior, we need a prescriptive or proscriptive norm against corruption. Without this, transparency just reinforces that everyone is accepting bribes and you’d be a fool not to. People who have lived in corrupt countries will have felt this frustration first hand. There’s a sense that it’s not about bad apples—the society is broken in ways that are sometimes difficult to articulate. But societal norms are not arbitrary. They are adapted to the local environment and influenced by historical contexts. In our experiment, the parameters created the environment. If there really is no easy way to legitimately make money and the state doesn’t have the power to punish free-riders, then bribery really is the right option. So even among Canadians, admittedly some of the nicest people in the world, in these in-game parameters, corruption was difficult to eradicate. When the country is poor and the state has no power, transparency doesn’t tell you not to pay a bribe, it solves a different problem—it tells you the price of the bribe. Not “should I pay”, but “how much”?

There were some other nuances to the experiment that deserve follow up. If we had played the game in Cameroon instead of Canada, we suspect baseline bribery would have been higher. Indeed, people with direct exposure to corruption norms encouraged more corruption in the game controlling for ethnic background. And those with an ethnic background that included more corrupt countries, but without direct exposure were actually better cooperators than the 3rd generation+ Canadians. These results may reveal some of the effects of migration and historical path dependence. Of course, great caution is required in applying these results to the messiness of the real world. We hope to further investigate these cultural patterns in future work. The experiment also reveals that corruption may be quite high in developed countries, but its costs aren’t as easily felt. Leaders in richer nations like the United States may accept “bribes” in the form of lobbying or campaign funding and these may indeed be costly for the efficiency of the economy, but it may be the difference between a city building 25 or 20 schools. In a poor country similar corruption may be the difference between a city building 3 or 1 school. Five is more than 3, but 3 is three times more than 1. In a rich nation, the cost of corruption may be larger in absolute value, but in a poorer nation, it may be larger in relative value and felt more acutely.

The take home is that cooperation and corruption are two sides of the same coin; different scales of cooperation competing. This approach gives us a powerful theoretical and empirical toolkit for developing a framework for understanding corruption, why some states succeed and others fail, why some oscillate, and the triggers that may lead to failed states succeeding and successful states failing. Our cultural evolutionary biases lead us to look for whom to learn from and perhaps whom to avoid. They lead us to blame individuals for corruption. But just as atrocities are the acts of many humans cooperating toward an evil end, corruption is a feature of a society not individuals. Indeed, corruption is arguably easier to understand than my fearless acceptance of my anonymous barista’s coffee. Our tendency to favor those who share copies of our genes—a tendency all animals share—lead to both love of family and nepotism. Putting our buddies before others is as ancient as our species, but it creates inefficiencies in a meritocracy. Innovations are often the result of applying well-established approaches in one area to the problems of another. We hope the science of cooperation and cultural evolution will give us new tools in combating corruption.

1 Putting aside what it means for something to be natural for our species, suffice to say these are recent inventions in our evolutionary history, by no means culturally universal, and not shared by our closest cousins.

4 The United Nations Human Development Index ranks the United States 10th in the world. Liberia is 177th.

5Temporal discounting the degree to which we value the future less than the present. Our tendency to value the present over the future is one reason we don’t yet have Moon or Mars colonies, but the degree to which we do this varies from society to society.

6 Written laws can serve a signaling and coordination function; rather than having to interpret norms from the environment. When previously contentious norms are sufficiently well established, you may do well to codify them in law (legislating before they are established might mean more punishment—consider the history of prohibition in the United States).

7 Not that agriculture is the main reason for these cultural differences!

10 Singapore’s leaders are the highest paid in the world, but the nation also has one of the lowest corruption rates in the world—lower than the Netherlands, Canada, Germany, UK, Australia, and United States [source].

11 Note, there are some conceptual issues that make interpretation of the Singaporean treatment ambiguous. We discuss this in the supplementary. We’ll have to further explore this in a future study. Such is science.

To very briefly summarize, innovation is often assumed to be an individual endeavor driven by geniuses and then passed on to the masses. Consider Thomas Edison and the lightbulb or Gutenberg and the printing press. We argue that rather than a result of far-sighted geniuses, innovations are an emergent property of our species’ cultural learning abilities, applied within our societies and social networks. Our societies and social networks act as collective brains.

Innovations, large or small, do not require heroic geniuses any more than your thoughts hinge on a particular neuron.

We discuss some of the forces that affect these factors. These factors can also shape each other. For example, we provide preliminary evidence that transmission efficiency is affected by sociality—languages with more speakers are more efficient.

We argue that collective brains can make each of their constituent cultural brains more innovative. This perspective sheds light on traits, such as IQ, that have been implicated in innovation. A collective brain perspective can help us understand otherwise puzzling findings in the IQ literature, including group differences, heritability differences, and the dramatic increase in IQ test scores over time.

The chapter provides a brief overview of the science of cultural evolution, including its psychological foundations and implications. We discuss how humans evolved a second-line of inheritance, crossing the threshold into a world of cumulative culture. We begin by asking how culture can evolve, dispelling the mythical requirement of discrete genes and exact replication.

Evolutionary adaptation has three basic requirements: (1) individuals vary, (2) this variability is heritable (information transmission occurs), and (3) some variants are more likely to survive and spread than others. Genes have these characteristics so they evolve and adaptive. Culture also meets all three requirements, but in different ways. Like bacterial genes, cultural information spreads horizontally and need not be limited to parental transmission to offspring.

We discuss the evolution of our capacity for culture, asking how and when capacities for culture will evolve (when is cultural learning genetically adaptive).

The answer: culture is adaptive when asocial learning is hard and environments fluctuate a lot, but not too much.

We also outline the evolution of some of our social learning biases (a large part of the third requirement of an evolutionary system):

Some examples in the real world, such as the social spread of suicides (Werther effect) and literally learning better from same-sex and same-race instructors.

Content biases on what to learn: e.g. animals and plants, dangers, fire, reputation, social norms, and social groupings.

Cultural evolution shapes the beliefs and behaviors of groups so that they come adapted to the local environment (including culture) over time, shaping preferences and psychology.

Turning to the population-level, we explain why sociality influences cultural complexity (larger, more interconnected populations have more terms and technologies), how cultural evolution can lead to maladaptive behavior, and how intergroup competition can help eliminate these maladaptive behaviors, briefly discussing the viability of cultural-group selection.

Finally, we discuss how genes can adapt to culture: culture-gene coevolution and how this process may have led to the rapid expansion of the human brain.

Conformist transmission is a type of frequency dependent social learning
strategy in which individuals are disproportionately inclined to copy the most common trait in their sample of the population (e.g. individuals have a 90% probability of copying a trait that 60% of people possess). The bias is particularly important, because it tends to homogenize behavior within groups increasing between group differences relative to within group differences.

Our three key findings across two experiments were:

Substantial amounts of conformist transmission. We found substantial reliance on conformist biased social learning, with only 3% and 9% (or 15%) showing no bias in Experiments 1 and 2, respectively.

Increased social learning and stronger conformist bias as the number of options increased. Both the amount of social learning and the strength of conformist biases increased as the number of options increased (i.e. 60% of people wearing black shirts is more persuasive in a world of black, red, blue, yellow, and white shirt colors than in a world of only black shirts and white shirts). These results mean that all prior experiments have underestimated reliance on social learning and the strength of conformist transmission, since all use only 2 options.

IQ predicts both social learning and the strength of the conformist bias. IQ predicts less social learning, but has a U-shaped relationship to the strength of the conformist bias. These results suggest that higher IQ individuals are strategically using social learning (using it less, but with a stronger conformist bias when they choose to use other information).

Proceedings of the Royal Society B: Biological Sciences published my paper with Ben W. Shulman, Vlad Vasilescu, and Joe Henrich showing that sociality influences cultural complexity. Across two experiments, we show that access to more people (1) increases cultural complexity, allowing for cumulative cultural evolution and (2) reduces the loss of cultural knowledge and skill. We found that students paid most attention to the most capable of their mentors, but also drew inspiration from the others, suggesting that the benefit of greater interconnectivity is twofold: you have access to the best people and information, but are also able to recombine knowledge from a greater variety of people.